{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "grep: warning: GREP_OPTIONS is deprecated; please use an alias or script\n",
      "\u001b[32mUsing official model (PP-OCRv5_server_rec), the model files will be automatically downloaded and saved in /root/.paddlex/official_models.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from paddlex import create_model\n",
    "import cv2\n",
    "\n",
    "model_det_xuanze = create_model(model_name=\"PP-YOLOE_plus-L\",model_dir=\"output/dataset_xuanze_1000_test/best_model/inference\",)\n",
    "# model = create_model(model_name=\"PP-YOLOE_plus-L\",model_dir=\"output/dataset_xuanze_1000/269/inference\",)\n",
    "model_ocr_xuanze = create_model(model_name=\"PP-OCRv5_server_rec\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "def output_det_xuanze(img):\n",
    "    output_dict_res={}\n",
    "    output_det_xuanze = model_det_xuanze.predict(img,batch_size=1,threshold=0.7)\n",
    "    for det_res in output_det_xuanze:\n",
    "        output_dict_res=det_res.json[\"res\"]\n",
    "        boxes=output_dict_res[\"boxes\"]\n",
    "        for boxe in boxes:\n",
    "            boxe[\"coordinate\"]=[int(i) for i in boxe[\"coordinate\"]]\n",
    "            coordinate=boxe[\"coordinate\"]\n",
    "            if boxe[\"cls_id\"]==1:\n",
    "                color=(255,0,0)\n",
    "            else:\n",
    "                color=(0,255,0)\n",
    "                # ocr_img=cropped_img(img,coordinate)\n",
    "                # boxe[\"text\"]=output_ocr_xuanze(ocr_img)\n",
    "            img = cv2.rectangle(img, coordinate[:2], coordinate[2:], color, 2)\n",
    "            # print(boxe)\n",
    "        \n",
    "        # cv2.imwrite(\"/paddle/PaddleX/output/train_result.png\",img)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.16s)\n",
      "creating index...\n",
      "index created!\n",
      "Image ID: 771, File Name: options_0607.png\n",
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      "Image ID: 607, File Name: options_0078.png\n"
     ]
    }
   ],
   "source": [
    "from pycocotools.coco import COCO\n",
    "import skimage.io as io\n",
    "import os\n",
    "\n",
    "# 初始化COCO API\n",
    "dataDir = '/paddle/PaddleX/dataset/dataset_xuanze_1000/'  # 替换为你的COCO数据集路径\n",
    "dataType = 'val'        # 或者 'train2017', 'test2017'\n",
    "annFile = '{}/annotations/instance_{}.json'.format(dataDir, dataType)\n",
    "\n",
    "coco = COCO(annFile)\n",
    "\n",
    "# 获取所有图片的ID\n",
    "imgIds = coco.getImgIds()\n",
    "\n",
    "# 遍历每一张图片\n",
    "for imgId in imgIds:\n",
    "    # 加载图片信息\n",
    "    img = coco.loadImgs(imgId)[0]\n",
    "    \n",
    "    # 构建图片的完整路径\n",
    "    img_path = os.path.join(dataDir, \"images\", img['file_name'])\n",
    "    img_out_path=img_path.replace(\"images\",\"images_res\")\n",
    "    # print(img_path)\n",
    "    \n",
    "    # 读取图像\n",
    "    img_src = cv2.imread(img_path)\n",
    "    img_res=output_det_xuanze(img_src)\n",
    "    cv2.imwrite(img_out_path,img_res)\n",
    "\n",
    "\n",
    "    \n",
    "    # 打印一些图片信息\n",
    "    print(f\"Image ID: {img['id']}, File Name: {img['file_name']}\")\n",
    "    \n",
    "    # # 如果你还想获取该图片的标注信息\n",
    "    # annIds = coco.getAnnIds(imgIds=img['id'])\n",
    "    # anns = coco.loadAnns(annIds)\n",
    "    # for ann in anns:\n",
    "    #     print(f\"Annotation ID: {ann['id']}, Category ID: {ann['category_id']}, Bbox: {ann['bbox']}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'cls_id': 1, 'label': '选项', 'score': 0.9494935274124146, 'coordinate': [427, 70, 461, 92]}\n",
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      "{'cls_id': 1, 'label': '选项', 'score': 0.9490945339202881, 'coordinate': [749, 334, 781, 356]}\n",
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0.9382843375205994, 'coordinate': [791, 370, 824, 392]}, {'cls_id': 1, 'label': '选项', 'score': 0.9379141926765442, 'coordinate': [791, 334, 824, 356]}, {'cls_id': 1, 'label': '选项', 'score': 0.9373760223388672, 'coordinate': [1029, 229, 1062, 251]}, {'cls_id': 1, 'label': '选项', 'score': 0.9373424053192139, 'coordinate': [65, 175, 99, 197]}, {'cls_id': 1, 'label': '选项', 'score': 0.9371466040611267, 'coordinate': [184, 369, 217, 391]}, {'cls_id': 1, 'label': '选项', 'score': 0.9371024966239929, 'coordinate': [59, 262, 93, 284]}, {'cls_id': 1, 'label': '选项', 'score': 0.9370155930519104, 'coordinate': [1322, 144, 1356, 165]}, {'cls_id': 1, 'label': '选项', 'score': 0.9365724921226501, 'coordinate': [184, 262, 217, 284]}, {'cls_id': 1, 'label': '选项', 'score': 0.9365589618682861, 'coordinate': [469, 141, 502, 162]}, {'cls_id': 1, 'label': '选项', 'score': 0.9361923336982727, 'coordinate': [65, 105, 98, 127]}, {'cls_id': 1, 'label': '选项', 'score': 0.9360647797584534, 'coordinate': [426, 141, 461, 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125], 'text': '3'}, {'cls_id': 0, 'label': '题号', 'score': 0.6411551833152771, 'coordinate': [220, 73, 231, 88], 'text': '7'}, {'cls_id': 0, 'label': '题号', 'score': 0.5478744506835938, 'coordinate': [45, 147, 54, 161], 'text': '4'}, {'cls_id': 0, 'label': '题号', 'score': 0.5279483199119568, 'coordinate': [45, 179, 54, 195], 'text': 'S'}]}\n"
     ]
    }
   ],
   "source": [
    "# 加载验证集\n",
    "dataset = paddlex.datasets.CocoDetection(\n",
    "    data_dir='path/to/coco',  # COCO 数据集目录\n",
    "    ann_file='annotations/instances_val2017.json',  # 验证集标注文件\n",
    "    transforms=eval_transforms,\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "# 创建保存错误预测结果的文件夹\n",
    "output_dir = 'wrong_predictions'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# 开始评估并保存错误预测\n",
    "for idx, sample in enumerate(dataset):\n",
    "    img_path = sample['image']\n",
    "    img = cv2.imread(img_path)\n",
    "    \n",
    "    result = model.predict(img_path)\n",
    "    \n",
    "    # 获取真实标签\n",
    "    gt_labels = sample['gt_bbox'], sample['gt_class']\n",
    "    \n",
    "    # 这里简化了比较逻辑，实际情况可能更复杂\n",
    "    if not compare_results(result, gt_labels):  # 假设compare_results函数实现了比较逻辑\n",
    "        # 保存图像及预测信息\n",
    "        output_img_path = os.path.join(output_dir, f\"wrong_{idx}.jpg\")\n",
    "        draw_predictions(img, result, gt_labels, output_img_path)\n",
    "\n",
    "print(\"检查完成，错误预测已保存至:\", output_dir)\n",
    "\n",
    "def compare_results(pred_result, gt_labels):\n",
    "    \"\"\"简单比较预测结果和真实标签是否匹配\"\"\"\n",
    "    # 这里需要根据实际情况实现具体的比较逻辑\n",
    "    pass\n",
    "\n",
    "def draw_predictions(img, pred_result, gt_labels, output_path):\n",
    "    \"\"\"在图像上绘制预测框和真实框，并保存\"\"\"\n",
    "    for bbox in pred_result:\n",
    "        x1, y1, x2, y2 = map(int, bbox['bbox'])\n",
    "        score = bbox['score']\n",
    "        label = bbox['category_id']\n",
    "        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)  # 绿色预测框\n",
    "        cv2.putText(img, f\"{label}:{score:.2f}\", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n",
    "    \n",
    "    for bbox, label in zip(*gt_labels):\n",
    "        x1, y1, w, h = map(int, bbox)\n",
    "        x2, y2 = x1 + w, y1 + h\n",
    "        cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)  # 蓝色真实框\n",
    "    \n",
    "    cv2.imwrite(output_path, img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'cls_id': 1, 'label': '选项', 'score': 0.9536740183830261, 'coordinate': [627, 42, 661, 56]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9514209628105164, 'coordinate': [510, 100, 544, 116]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9513667821884155, 'coordinate': [789, 42, 823, 56]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.947320282459259, 'coordinate': [179, 72, 212, 87]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9471486806869507, 'coordinate': [736, 41, 769, 56]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9452840685844421, 'coordinate': [509, 42, 544, 57]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9443469047546387, 'coordinate': [123, 72, 159, 88]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9442337155342102, 'coordinate': [233, 72, 267, 88]}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9442254900932312, 'coordinate': [179, 101, 213, 117]}\n",
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      "{'cls_id': 1, 'label': '选项', 'score': 0.9370014071464539, 'coordinate': [400, 71, 435, 86]}\n",
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      "{'cls_id': 1, 'label': '选项', 'score': 0.9292673468589783, 'coordinate': [178, 43, 212, 58]}\n",
      "{'cls_id': 0, 'label': '题号', 'score': 0.9292531609535217, 'coordinate': [584, 99, 601, 114], 'text': '2'}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9279848337173462, 'coordinate': [123, 43, 158, 59]}\n",
      "{'cls_id': 0, 'label': '题号', 'score': 0.9265985488891602, 'coordinate': [584, 70, 602, 85], 'text': '15'}\n",
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      "{'cls_id': 0, 'label': '题号', 'score': 0.9229785799980164, 'coordinate': [585, 41, 602, 56], 'text': '12'}\n",
      "{'cls_id': 1, 'label': '选项', 'score': 0.9211030602455139, 'coordinate': [347, 71, 379, 86]}\n",
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     ]
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "def output_det_xuanze(img):\n",
    "    output_dict_res={}\n",
    "    output_det_xuanze = model_det_xuanze.predict(img,batch_size=1,threshold=0.7)\n",
    "    for det_res in output_det_xuanze:\n",
    "        output_dict_res=det_res.json[\"res\"]\n",
    "        boxes=output_dict_res[\"boxes\"]\n",
    "        for boxe in boxes:\n",
    "            boxe[\"coordinate\"]=[int(i) for i in boxe[\"coordinate\"]]\n",
    "            coordinate=boxe[\"coordinate\"]\n",
    "            if boxe[\"cls_id\"]==1:\n",
    "                color=(255,0,0)\n",
    "            else:\n",
    "                color=(0,255,0)\n",
    "                ocr_img=cropped_img(img,coordinate)\n",
    "                boxe[\"text\"]=output_ocr_xuanze(ocr_img)\n",
    "            img = cv2.rectangle(img, coordinate[:2], coordinate[2:], color, 2)\n",
    "            print(boxe)\n",
    "        \n",
    "        # cv2.imwrite(\"/paddle/PaddleX/output/train_result.png\",img)\n",
    "    return img\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import os\n",
    "    \n",
    "# 调用示例\n",
    "list_file=[]\n",
    "list_file_res=[]\n",
    "directory = \"/paddle/PaddleX/output/xuanze_test_img\"\n",
    "directory_res= \"/paddle/PaddleX/output/test_img_re\"\n",
    "for root, dirs, files in os.walk(directory):\n",
    "    for file in files:\n",
    "        if \".png\" in file:\n",
    "            file_path=os.path.join(root, file)\n",
    "            list_file.append(file_path)\n",
    "            list_file_res.append(os.path.join(directory_res, file))\n",
    "\n",
    "for img_i,img_path in enumerate(list_file):\n",
    "    # 读取图像\n",
    "    img = cv2.imread(img_path)\n",
    "    img_res=output_det_xuanze(img)\n",
    "    cv2.imwrite(list_file_res[img_i],img_res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'image_base64': 'img', 'pic_url': 'test_test.py', 'res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 1, 'label': '选项', 'score': 0.9632763266563416, 'coordinate': [1167, 108, 1199, 130]}, {'cls_id': 1, 'label': '选项', 'score': 0.9630897641181946, 'coordinate': [1111, 335, 1146, 358]}, {'cls_id': 1, 'label': '选项', 'score': 0.9628348350524902, 'coordinate': [1407, 108, 1440, 130]}, {'cls_id': 1, 'label': '选项', 'score': 0.9627404808998108, 'coordinate': [1407, 144, 1440, 165]}, {'cls_id': 1, 'label': '选项', 'score': 0.9627400040626526, 'coordinate': [1111, 371, 1146, 394]}, {'cls_id': 1, 'label': '选项', 'score': 0.9625367522239685, 'coordinate': [1111, 299, 1146, 322]}, {'cls_id': 1, 'label': '选项', 'score': 0.9621376991271973, 'coordinate': [505, 371, 538, 393]}, {'cls_id': 1, 'label': '选项', 'score': 0.9616036415100098, 'coordinate': [505, 265, 538, 287]}, {'cls_id': 1, 'label': '选项', 'score': 0.9611761569976807, 'coordinate': [1166, 37, 1199, 59]}, {'cls_id': 1, 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[44, 111, 54, 125], 'text': '3'}, {'cls_id': 0, 'label': '题号', 'score': 0.6401315927505493, 'coordinate': [220, 73, 231, 88], 'text': '7'}, {'cls_id': 0, 'label': '题号', 'score': 0.5555759072303772, 'coordinate': [45, 147, 54, 161], 'text': '4'}, {'cls_id': 0, 'label': '题号', 'score': 0.5307167768478394, 'coordinate': [45, 179, 54, 195], 'text': '5'}]}} <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "import base64,requests,json,cv2\n",
    "\n",
    "def output_det_xuanze(img):\n",
    "    output_dict_res={}\n",
    "    output_det_xuanze = model_det_xuanze.predict(img,batch_size=1,threshold=0.7)\n",
    "    for det_res in output_det_xuanze:\n",
    "        output_dict_res=det_res.json[\"res\"]\n",
    "        boxes=output_dict_res[\"boxes\"]\n",
    "        for boxe in boxes:\n",
    "            boxe[\"coordinate\"]=[int(i) for i in boxe[\"coordinate\"]]\n",
    "            coordinate=boxe[\"coordinate\"]\n",
    "            if boxe[\"cls_id\"]==1:\n",
    "                color=(255,0,0)\n",
    "            else:\n",
    "                color=(0,255,0)\n",
    "                ocr_img=cropped_img(coordinate)\n",
    "                boxe[\"text\"]=output_ocr_xuanze(ocr_img)\n",
    "            img = cv2.rectangle(img, coordinate[:2], coordinate[2:], color, 2)\n",
    "            print(boxe)\n",
    "        \n",
    "        # cv2.imwrite(\"/paddle/PaddleX/output/train_result.png\",img)\n",
    "    return img\n",
    "\n",
    "\n",
    "\n",
    "server_url=\"http://127.0.0.1:8080\"\n",
    "\n",
    "directory = \"/paddle/PaddleX/output/xuanze_test_img/xuanze001.png\"\n",
    "# 读取图像\n",
    "img = cv2.imread(directory)\n",
    "ret, base64_image = cv2.imencode(\".jpg\", img)\n",
    "base64_image = str(base64.b64encode(base64_image))[2:-1]\n",
    "\n",
    "payload = {\n",
    "            \"image_base64\": base64_image,\n",
    "            \"pic_url\": \"test_test.py\",\n",
    "        }\n",
    "# payload=json.dumps(payload)\n",
    "\n",
    "session = requests.session()\n",
    "session.keep_alive = False\n",
    "response = session.post(server_url + \"/predict\", data=payload)\n",
    "result = response.text if response.status_code == 200 else response.status_code\n",
    "\n",
    "result=json.loads(result)\n",
    "print(result,type(result))\n",
    "\n",
    "\n"
   ]
  }
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